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Pragmatic AI: An Introduction to Cloud-Based Machine Learning

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Pragmatic AI: An Introduction to Cloud-Based Machine Learning

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  • About eBook Formats
  • This eBook includes the following formats, accessible from your Account page after purchase:

    ePub EPUB The open industry format known for its reflowable content and usability on supported mobile devices.

    MOBI MOBI The eBook format compatible with the Amazon Kindle and Amazon Kindle applications.

    Adobe Reader PDF The popular standard, used most often with the free Adobe® Reader® software.

    This eBook requires no passwords or activation to read. We customize your eBook by discreetly watermarking it with your name, making it uniquely yours.

About

Features

  • Illuminates the latest solutions from Google, Amazon, and Microsoft, plus powerful techniques based on Python and R
  • Real-world examples and workflows show how machine learning, AI, and cloud computing come together in real business solutions
  • Covers every step from deployment to production, focusing on cloud environments that maximize scalability and simplify deployment
  • Addresses coding, libraries, statistical techniques, modeling for fast prototyping, and more
  • Description

    • Copyright 2019
    • Dimensions: 7" x 9-1/8"
    • Pages: 256
    • Edition: 1st
    • Book
    • ISBN-10: 0-13-486386-0
    • ISBN-13: 978-0-13-486386-3

    Master Powerful Off-the-Shelf Business Solutions for AI and Machine Learning


    Pragmatic AI will help you solve real-world problems with contemporary machine learning, artificial intelligence, and cloud computing tools. Noah Gift demystifies all the concepts and tools you need to get results—even if you don’t have a strong background in math or data science.

    Gift illuminates powerful off-the-shelf cloud offerings from Amazon, Google, and Microsoft, and demonstrates proven techniques using the Python data science ecosystem. His workflows and examples help you streamline and simplify every step, from deployment to production, and build exceptionally scalable solutions. As you learn how machine language (ML) solutions work, you’ll gain a more intuitive understanding of what you can achieve with them and how to maximize their value.

    Building on these fundamentals, you’ll walk step-by-step through building cloud-based AI/ML applications to address realistic issues in sports marketing, project management, product pricing, real estate, and beyond. Whether you’re a business professional, decision-maker, student, or programmer, Gift’s expert guidance and wide-ranging case studies will prepare you to solve data science problems in virtually any environment.

    • Get and configure all the tools you’ll need
    • Quickly review all the Python you need to start building machine learning applications
    • Master the AI and ML toolchain and project lifecycle
    • Work with Python data science tools such as IPython, Pandas, Numpy, Juypter Notebook, and Sklearn
    • Incorporate a pragmatic feedback loop that continually improves the efficiency of your workflows and systems
    • Develop cloud AI solutions with Google Cloud Platform, including TPU, Colaboratory, and Datalab services
    • Define Amazon Web Services cloud AI workflows, including spot instances, code pipelines, boto, and more
    • Work with Microsoft Azure AI APIs
    • Walk through building six real-world AI applications, from start to finish

    Register your book for convenient access to downloads, updates, and/or corrections as they become available. See inside book for details.

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    Sample Content

    Table of Contents

    Foreword

    Preface

    Acknowledgments

    About the Author

    Part I: Introduction to Pragmatic AI

    Chapter 1: Introduction to Pragmatic AI

    Chapter 2: AI and ML Toolchain

    Chapter 3: Spartan AI Lifecycle

    Part II: AI in the Cloud

    Chapter 4: Cloud AI Development with Google Cloud Platform

    Chapter 5: Cloud Ai Development with Amazon Web Services

    Part III: Creating Practical AI Applications from Scratch

    Chapter 6: Predicting Social-Media Influence in the NBA

    Chapter 7: Creating an Intelligent Slackbot on AWS

    Chapter 8: Finding Project Management Insights from a Github Organization

    Chapter 9: Dynamically Optimizing EC2 Instances on AWS

    Chapter 10: Real Estate

    Chapter 11: Production AI for User Generated Content (UGC)

    Appendix A: AI Accelerators

    Appendix B: Deciding on Cluster Size

    Index

    Updates

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